Abstract:This study develops a reconstructed typhoon disaster database over a longer period 1980–2018 and at higher spatial resolution by using the county-level resolution disaster records for the eight coastal provinces of China during the shorter period 2004–2013. Typhoon precipitation and wind data over the shorter period are matched to disaster data and a functional relationship is derived between typhoon meteorological impact and typhoon disaster (direct economic loss). This relationship is used to construct a dis… Show more
“…This relationship is used to construct a disaster dataset for a longer period. What needs to be further explained is that the missing values of wind speed data reduced significantly after 1980, the difficulty for wind speed data acquisition and the complexity and time consumption of data processing restricted the time period of the study to 1980–2018 47 . Figure 2 Spatial distributions of the 505 stations over the eight coastal provinces (i.e., Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan) of China (cited from Wu et al 47 ).…”
Section: Data and Experimental Designmentioning
confidence: 99%
“… Spatial distributions of the 505 stations over the eight coastal provinces (i.e., Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan) of China (cited from Wu et al 47 ). …”
Section: Data and Experimental Designmentioning
confidence: 99%
“…(iv) The training sample size should be larger than the independent sample size, and typhoons in the former should be relatively long-lived in order to ensure that the results are universally applicable. Meanwhile, considering the actual records for the period from 2004 to 2013 47 , as a result, 3 at C5, 16 at C4 and 11 at C3 damage category landfalling typhoons from 2004 to 2011 are selected as training samples (Fig. 3 a), and 2 at C5, 12 at C4, 7 at C3 damage category landfalling typhoons from 2012 to 2015 as independent samples (Fig.…”
In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.
“…This relationship is used to construct a disaster dataset for a longer period. What needs to be further explained is that the missing values of wind speed data reduced significantly after 1980, the difficulty for wind speed data acquisition and the complexity and time consumption of data processing restricted the time period of the study to 1980–2018 47 . Figure 2 Spatial distributions of the 505 stations over the eight coastal provinces (i.e., Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan) of China (cited from Wu et al 47 ).…”
Section: Data and Experimental Designmentioning
confidence: 99%
“… Spatial distributions of the 505 stations over the eight coastal provinces (i.e., Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan) of China (cited from Wu et al 47 ). …”
Section: Data and Experimental Designmentioning
confidence: 99%
“…(iv) The training sample size should be larger than the independent sample size, and typhoons in the former should be relatively long-lived in order to ensure that the results are universally applicable. Meanwhile, considering the actual records for the period from 2004 to 2013 47 , as a result, 3 at C5, 16 at C4 and 11 at C3 damage category landfalling typhoons from 2004 to 2011 are selected as training samples (Fig. 3 a), and 2 at C5, 12 at C4, 7 at C3 damage category landfalling typhoons from 2012 to 2015 as independent samples (Fig.…”
In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.
“…Each record of a typhoon disaster case includes the station name, disaster event and the corresponding damage in terms of direct economic losses. This long-period of database was developed from a reliable, available, and county-level disaster dataset by the National Climate Centre of China Meteorological Administration (CMA) 44 .…”
Section: Datamentioning
confidence: 99%
“…Damage (¥0.1 billion) classi cations associated with each typhoon case at individual stations (cited from Wu et al44 ).…”
In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: i) obtaining the forecast track of a target landfalling typhoon, ii) constructing its generalized initial value (GIV), iii) identifying its analogs based on the GIV, and iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.
Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process–Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and comparison of the disaster risk caused by two typhoons, Maysak and Haishen, in Jilin Province. Game theory enhances precision in evaluation beyond conventional approaches, effectively addressing the shortcomings of both subjective and objective weighting methods. Typhoon Maysak and Typhoon Haishen exhibit analogous tracks. They have successively exerted an impact on Jilin Province, and the phenomenon of overlapping rain areas is a crucial factor in triggering disasters. Typhoon Maysak features stronger wind force and greater hourly rainfall intensity, while Typhoon Haishen has a longer duration of rainfall. Additionally, Typhoon Maysak causes more severe disasters in Jilin Province. With regard to the four dimensions of disaster risk, the analysis of hazards reveals that the areas categorized as high risk and above in relation to the two typhoons are mainly located in the central-southern and eastern regions of Jilin Province. Typhoon Maysak has a slightly higher hazard level. During the exposure assessment, it was determined that the high-risk areas occupied 16% of the gross area of Jilin Province. It is mainly concentrated in three economically developed cities, as well as some large agricultural counties. In the context of vulnerability analysis, regions classified as high risk and above constitute 54% of the overall area. The areas classified as having high vulnerability are predominantly located in Yushu, Nong’an, and Songyuan. From the analysis of emergency response and recovery ability, Changchun has strong typhoon disaster prevention and reduction ability. This is proportional to the local level of economic development. The mountainous areas in the east and the regions to the west are comparatively weak. Finally, the comprehensive typhoon disaster risk zoning indicates that the zoning of the two typhoons is relatively comparable. When it comes to high-risk and above areas, Typhoon Maysak accounts for 38% of the total area, while Typhoon Haishen occupies 47%. The regions with low risk are predominantly found in Changchun, across the majority of Baicheng, and at the intersection of Baishan and Jilin. Upon comparing the disasters induced by two typhoons in Jilin Province, it was observed that the disasters caused by Typhoon Maysak were considerably more severe than those caused by Typhoon Haishen. This finding aligns with the intense wind and heavy rainfall brought by Typhoon Maysak.
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